Deep learning and its applications in nuclear magnetic resonance spectroscopy
Copyright © 2024 Elsevier B.V. All rights reserved.
| Veröffentlicht in: | Progress in nuclear magnetic resonance spectroscopy. - 1998. - 146-147(2025) vom: 01. Apr., Seite 101556 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2025
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| Zugriff auf das übergeordnete Werk: | Progress in nuclear magnetic resonance spectroscopy |
| Schlagworte: | Journal Article Review Deep learning In vivo magnetic resonance spectroscopy (MRS) Nano nuclear magnetic resonance (NanoNMR) Nuclear magnetic resonance (NMR) |
| Zusammenfassung: | Copyright © 2024 Elsevier B.V. All rights reserved. Nuclear Magnetic Resonance (NMR), as an advanced technology, has widespread applications in various fields like chemistry, biology, and medicine. However, issues such as long acquisition times for multidimensional spectra and low sensitivity limit the broader application of NMR. Traditional algorithms aim to address these issues but have limitations in speed and accuracy. Deep Learning (DL), a branch of Artificial Intelligence (AI) technology, has shown remarkable success in many fields including NMR. This paper presents an overview of the basics of DL and current applications of DL in NMR, highlights existing challenges, and suggests potential directions for improvement |
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| Beschreibung: | Date Revised 30.04.2025 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1873-3301 |
| DOI: | 10.1016/j.pnmrs.2024.101556 |